Data Cleaning - Geospatial Data

import pandas as pd
import os
import geopandas as gpd
import matplotlib.pyplot as plt
from geopy.geocoders import Nominatim
import folium
import ipywidgets as widgets
from IPython.display import display
# specify the directory containing the CSV files
directory = '../../data/quarterly_financials'

# create an empty list to store the dataframes
dfs = []

# loop over the CSV files in the directory
for filename in os.listdir(directory):
    if filename.endswith('.csv'):
        # read the CSV file into a dataframe and append it to the list
        path = os.path.join(directory, filename)
        df = pd.read_csv(path)
        dfs.append(df)

# concatenate the dataframes into a single dataframe
quarterly_financials = pd.concat(dfs, ignore_index=True)
# sort
quarterly_financials = quarterly_financials.sort_values(by=['REPDTE', 'CERT'], ascending=[False, True])
quarterly_financials = quarterly_financials.reset_index(drop=True)
# print the combined dataframe
display(quarterly_financials.shape)
display(quarterly_financials.head())
(971541, 15)
ZIP BKCLASS REPDTE DEPDOM ASSET STNAME EQ NAME CITY ADDRESS ENDEFYMD CERT ESTYMD LIAB ID
0 2111 SM 20221231 163284000.0 298020000 MASSACHUSETTS 26579000.0 STATE STREET BANK&TRUST CO BOSTON 1 LINCOLN ST 99991231.0 14 17920101 271441000.0 14_20221231
1 36830 SM 20221231 952037.0 1023366 ALABAMA 65967.0 AUBURNBANK AUBURN 100 N GAY ST 99991231.0 35 19070103 957399.0 35_20221231
2 36732 NM 20221231 407949.0 444822 ALABAMA 32577.0 ROBERTSON BANKING CO DEMOPOLIS 216 N WALNUT AVE 99991231.0 39 18700101 412245.0 39_20221231
3 36867 NM 20221231 266874.0 265272 ALABAMA -8465.0 PHENIX-GIRARD BANK PHENIX CITY 801 13TH ST 99991231.0 41 19040504 273737.0 41_20221231
4 36401 NM 20221231 70649.0 76239 ALABAMA 5487.0 BANK OF EVERGREEN EVERGREEN 146 W FRONT ST 99991231.0 49 19320901 70752.0 49_20221231 
display(len(quarterly_financials['BKCLASS'].unique()))
display(quarterly_financials['BKCLASS'].unique())
7
array(['SM', 'NM', 'N', 'SI', 'SB', 'SL', 'OI'], dtype=object)

Zip coordinates

# bring in
zip_coordinates = pd.read_csv('../../data/coordinate_data/2022_Gaz_zcta_national.txt', sep='\t')

# remove whitespace in col names
zip_coordinates.columns = [col.strip() for col in zip_coordinates.columns]

# drop unnecesary columns
zip_coordinates.drop(['ALAND', 'ALAND_SQMI', 'AWATER', 'AWATER_SQMI'], axis=1, inplace=True)

# check
display(zip_coordinates.shape)
display(zip_coordinates.head())
(33791, 3)
GEOID INTPTLAT INTPTLONG
0 601 18.180555 -66.749961
1 602 18.361945 -67.175597
2 603 18.457399 -67.124867
3 606 18.158327 -66.932928
4 610 18.293960 -67.127182 
display(len(quarterly_financials['ZIP'].unique()))
10754

Combine

# Rename the 'GEOID' column in 'zip_coordinates' to match the 'ZIP' column in 'quarterly_financials'
zip_coordinates = zip_coordinates.rename(columns={'GEOID': 'ZIP'})

# Merge the two dataframes on the 'ZIP' column
merged_df = quarterly_financials.merge(zip_coordinates, on='ZIP', how='left')

# Create a new column 'coordinates' with the combined 'INTPTLAT' and 'INTPTLONG' columns as a tuple
merged_df['zip_coordinates'] = list(zip(merged_df['INTPTLAT'], merged_df['INTPTLONG']))

# Update the 'quarterly_financials' dataframe with the new 'coordinates' column
quarterly_financials = merged_df

# Print the updated 'quarterly_financials' dataframe
display(quarterly_financials.shape)
display(quarterly_financials.head())
(971541, 18)
ZIP BKCLASS REPDTE DEPDOM ASSET STNAME EQ NAME CITY ADDRESS ENDEFYMD CERT ESTYMD LIAB ID INTPTLAT INTPTLONG zip_coordinates
0 2111 SM 20221231 163284000.0 298020000 MASSACHUSETTS 26579000.0 STATE STREET BANK&TRUST CO BOSTON 1 LINCOLN ST 99991231.0 14 17920101 271441000.0 14_20221231 42.350680 -71.060527 (42.35068, -71.060527)
1 36830 SM 20221231 952037.0 1023366 ALABAMA 65967.0 AUBURNBANK AUBURN 100 N GAY ST 99991231.0 35 19070103 957399.0 35_20221231 32.534872 -85.493755 (32.534872, -85.493755)
2 36732 NM 20221231 407949.0 444822 ALABAMA 32577.0 ROBERTSON BANKING CO DEMOPOLIS 216 N WALNUT AVE 99991231.0 39 18700101 412245.0 39_20221231 32.417456 -87.892213 (32.417456, -87.892213)
3 36867 NM 20221231 266874.0 265272 ALABAMA -8465.0 PHENIX-GIRARD BANK PHENIX CITY 801 13TH ST 99991231.0 41 19040504 273737.0 41_20221231 32.498054 -85.023590 (32.498054, -85.02359)
4 36401 NM 20221231 70649.0 76239 ALABAMA 5487.0 BANK OF EVERGREEN EVERGREEN 146 W FRONT ST 99991231.0 49 19320901 70752.0 49_20221231 31.468970 -86.950426 (31.46897, -86.950426) 
# removed unmatched zips
quarterly_financials.dropna(subset=['INTPTLAT'], inplace=True)

# drop unnecesary columns
quarterly_financials.drop(['INTPTLAT', 'INTPTLONG', 'ENDEFYMD', 'CERT', 'ESTYMD', 'LIAB', 'ID', 'ADDRESS', 'EQ', 'ZIP'], axis=1, inplace=True)

# convert numerical values to millions (source is in thousands)
quarterly_financials['DEPDOM'] = quarterly_financials['DEPDOM'].divide(1000)
quarterly_financials['ASSET'] = quarterly_financials['ASSET'].divide(1000)

# rename
quarterly_financials.rename(columns={
    'BKCLASS': 'bank_class', 
    'REPDTE': 'report_date', 
    'DEPDOM': 'deposits_mill', 
    'ASSET': 'assets_mill', 
    'STNAME': 'state', 
    'NAME': 'name',
    'CITY': 'city'
    }, inplace=True)

display(quarterly_financials.head())
bank_class report_date deposits_mill assets_mill state name city zip_coordinates
0 SM 20221231 163284.000 298020.000 MASSACHUSETTS STATE STREET BANK&TRUST CO BOSTON (42.35068, -71.060527)
1 SM 20221231 952.037 1023.366 ALABAMA AUBURNBANK AUBURN (32.534872, -85.493755)
2 NM 20221231 407.949 444.822 ALABAMA ROBERTSON BANKING CO DEMOPOLIS (32.417456, -87.892213)
3 NM 20221231 266.874 265.272 ALABAMA PHENIX-GIRARD BANK PHENIX CITY (32.498054, -85.02359)
4 NM 20221231 70.649 76.239 ALABAMA BANK OF EVERGREEN EVERGREEN (31.46897, -86.950426) 
# change to title case
quarterly_financials[['state', 'name', 'city']] = quarterly_financials[['state', 'name', 'city']].apply(lambda x: x.str.title())

# format date
quarterly_financials['report_date'] = pd.to_datetime(quarterly_financials['report_date'], format='%Y%m%d')

# set to float
quarterly_financials['deposits_mill'] = quarterly_financials['deposits_mill'].astype(float)
quarterly_financials['assets_mill'] = quarterly_financials['assets_mill'].astype(float)


display(quarterly_financials.head())
bank_class report_date deposits_mill assets_mill state name city zip_coordinates
0 SM 2022-12-31 163284.000 298020.000 Massachusetts State Street Bank&Trust Co Boston (42.35068, -71.060527)
1 SM 2022-12-31 952.037 1023.366 Alabama Auburnbank Auburn (32.534872, -85.493755)
2 NM 2022-12-31 407.949 444.822 Alabama Robertson Banking Co Demopolis (32.417456, -87.892213)
3 NM 2022-12-31 266.874 265.272 Alabama Phenix-Girard Bank Phenix City (32.498054, -85.02359)
4 NM 2022-12-31 70.649 76.239 Alabama Bank Of Evergreen Evergreen (31.46897, -86.950426) 
quarterly_financials['bank_class'] = quarterly_financials['bank_class'].replace({
    'N':  'Commercial bank, national charter, Fed member',
    'NM': 'Commercial bank, state charter, Fed non-member',
    'OI': 'Insured U.S. branch of a foreign chartered institution',
    'SB': 'Federal savings banks',
    'SI': 'State chartered stock savings banks',
    'SL': 'State chartered stock savings and loan association',
    'SM': 'Commercial bank, state charter, Fed member',
    'NC': 'Noninsured non-deposit commercial bank',
    'NS': 'Noninsured stock savings bank',
    'CU': 'State or federally chartered credit union',
    })

display(quarterly_financials.shape)
display(quarterly_financials.head())

(937293, 8)
bank_class report_date deposits_mill assets_mill state name city zip_coordinates
0 Commercial bank, state charter, Fed member 2022-12-31 163284.000 298020.000 Massachusetts State Street Bank&Trust Co Boston (42.35068, -71.060527)
1 Commercial bank, state charter, Fed member 2022-12-31 952.037 1023.366 Alabama Auburnbank Auburn (32.534872, -85.493755)
2 Commercial bank, state charter, Fed non-member 2022-12-31 407.949 444.822 Alabama Robertson Banking Co Demopolis (32.417456, -87.892213)
3 Commercial bank, state charter, Fed non-member 2022-12-31 266.874 265.272 Alabama Phenix-Girard Bank Phenix City (32.498054, -85.02359)
4 Commercial bank, state charter, Fed non-member 2022-12-31 70.649 76.239 Alabama Bank Of Evergreen Evergreen (31.46897, -86.950426) 
quarterly_financials['zip_coordinates'].isna().sum()
0

Growth of Assets with Slider

from ipywidgets.embed import embed_minimal_html
import branca
from IPython.display import HTML


gdf = gpd.GeoDataFrame(quarterly_financials, geometry=gpd.points_from_xy(quarterly_financials.zip_coordinates.apply(lambda p: p[1]), quarterly_financials.zip_coordinates.apply(lambda p: p[0])))


def plot_bank_assets_by_date(date):
    # Filter the GeoDataFrame by the selected date
    filtered_gdf = gdf[gdf['report_date'] == date]

    # Group the filtered GeoDataFrame by state and sum the assets
    state_assets = filtered_gdf.groupby('state')['assets_mill'].sum().reset_index()

    # Create the base folium map
    m = folium.Map(location=[37.8, -96], zoom_start=4)

    # Define a function to scale the assets to a suitable size for the map
    def scale_bubble_size(assets):
        return assets / 50000

    # Plot bubbles for each state with a size proportional to the total assets
    for index, row in state_assets.iterrows():
        state_data = filtered_gdf[filtered_gdf['state'] == row['state']]
        state_centroid = state_data.unary_union.centroid
        folium.CircleMarker(
            location=[state_centroid.y, state_centroid.x],
            radius=scale_bubble_size(row['assets_mill']),
            color='blue',
            fill=True,
            fill_color='blue',
            fill_opacity=0.5,
            popup=f"State: {row['state']}<br>Total Assets: {row['assets_mill']:.0f}<br>Date: {date}"
        ).add_to(m)

    # Display the map
    return m



# Get the unique report dates sorted
unique_dates = sorted(quarterly_financials['report_date'].unique())

# Create the index slider
index_slider = widgets.IntSlider(
    min=0,
    max=len(unique_dates) - 1,
    description='Date Index:',
    continuous_update=False,
)

# Create the play button
play_button = widgets.Play(
    interval=100,  # Time in milliseconds between updates
    value=0,  # Slider starting value
    min=0,
    max=len(unique_dates) - 1,
    step=1,
    description="Press play",
    disabled=False
)

# Link the play button to the index slider
widgets.jslink((play_button, 'value'), (index_slider, 'value'))

# Create a horizontal box to display the slider and play button together
slider_with_play = widgets.HBox([index_slider, play_button])

def display_map(index):
    date = unique_dates[index]
    m = plot_bank_assets_by_date(date)
    display(m)

# Use widgets.interactive() and assign the output to a variable
interactive_map = widgets.interactive(display_map, index=index_slider)

# Clear the output of interactive_map (to prevent double display of the map)
interactive_map.update()


# Display the slider with the play button and the interactive map
display(slider_with_play)
display(interactive_map.children[-1])



import folium
import pandas as pd
import geopandas as gpd
from folium.plugins import HeatMap
from ipywidgets import widgets
from IPython.display import display, IFrame


gdf = gpd.GeoDataFrame(quarterly_financials, geometry=gpd.points_from_xy(quarterly_financials.zip_coordinates.apply(lambda p: p[1]), quarterly_financials.zip_coordinates.apply(lambda p: p[0])))


def plot_bank_assets_by_date(date):
    # Filter the GeoDataFrame by the selected date
    filtered_gdf = gdf[gdf['report_date'] == date]

    # Group the filtered GeoDataFrame by state and sum the assets
    state_assets = filtered_gdf.groupby('state')['assets_mill'].sum().reset_index()

    # Create the base folium map
    m = folium.Map(location=[37.8, -96], zoom_start=4)

    # Define a function to scale the assets to a suitable size for the map
    def scale_bubble_size(assets):
        return assets / 50000

    # Plot bubbles for each state with a size proportional to the total assets
    for index, row in state_assets.iterrows():
        state_data = filtered_gdf[filtered_gdf['state'] == row['state']]
        state_centroid = state_data.unary_union.centroid
        folium.CircleMarker(
            location=[state_centroid.y, state_centroid.x],
            radius=scale_bubble_size(row['assets_mill']),
            color='blue',
            fill=True,
            fill_color='blue',
            fill_opacity=0.5,
            popup=f"State: {row['state']}<br>Total Assets: {row['assets_mill']:.0f}<br>Date: {date}"
        ).add_to(m)

    return m


# Get the unique report dates sorted
unique_dates = sorted(quarterly_financials['report_date'].unique())

# Create the index slider
index_slider = widgets.IntSlider(
    min=0,
    max=len(unique_dates) - 1,
    description='Date Index:',
    continuous_update=False,
)

# Create the play button
play_button = widgets.Play(
    interval=100,  # Time in milliseconds between updates
    value=0,  # Slider starting value
    min=0,
    max=len(unique_dates) - 1,
    step=1,
    description="Press play",
    disabled=False
)

# Link the play button to the index slider
widgets.jslink((play_button, 'value'), (index_slider, 'value'))

# Create a horizontal box to display the slider and play button together
slider_with_play = widgets.HBox([index_slider, play_button])

def display_map(index):
    date = unique_dates[index]
    m = plot_bank_assets_by_date(date)
    display(IFrame(m._repr_html_(), width=700, height=600))

# Use widgets.interactive() and assign the output to a variable
interactive_map = widgets.interactive(display_map, index=index_slider)

# Clear the output of interactive_map (to prevent double display of the map)
interactive_map.update()

# Display the slider with the play button and the interactive map
display(slider_with_play)
display(interactive_map.children[-1])

Individual Points

Heatmap all dates

Latest date only

import folium
from folium.plugins import HeatMap
import pandas as pd
from IPython.display import IFrame

# Find the latest date
latest_date = quarterly_financials['report_date'].max()

# Filter the DataFrame by the latest date
latest_quarterly_financials = quarterly_financials[quarterly_financials['report_date'] == latest_date]

# Create a base map
map = folium.Map(location=[37.8, -96], zoom_start=4)

# Prepare the data for the HeatMap
heatmap_data = []
for index, row in latest_quarterly_financials.iterrows():
    coordinates = row['zip_coordinates']
    assets_mill = row['assets_mill']
    heatmap_data.append((*coordinates, assets_mill))

# Add the HeatMap to the map
heatmap = HeatMap(heatmap_data, radius=13, max_zoom=13, gradient={0.2: 'blue', 0.4: 'lime', 0.6: 'orange', 1: 'red'})
map.add_child(heatmap)

# For rendering on Quarto
map.save('heatmap_latest_date.html')
IFrame(src='heatmap_latest_date.html', width=700, height=600)

# For rendering in Jupyter Notebook
display(map)
Make this Notebook Trusted to load map: File -> Trust Notebook
import folium
from folium.plugins import HeatMap
import pandas as pd
from IPython.display import IFrame

# Find the latest date
latest_date = quarterly_financials['report_date'].max()

# Filter the DataFrame by the latest date
latest_quarterly_financials = quarterly_financials[quarterly_financials['report_date'] == latest_date]

# Create a base map
map = folium.Map(location=[37.8, -96], zoom_start=4)

# Prepare the data for the HeatMap
heatmap_data = []
for index, row in latest_quarterly_financials.iterrows():
    coordinates = row['zip_coordinates']
    assets_mill = row['assets_mill']
    heatmap_data.append((*coordinates, assets_mill))

# Add the HeatMap to the map
heatmap = HeatMap(heatmap_data, radius=10, max_zoom=13, gradient={0.0: '#ffffb2', 0.25: '#fecc5c', 0.5: '#fd8d3c', 0.75: '#f03b20', 1.0: '#bd0026'}) # YlOrBr color scheme
map.add_child(heatmap)

# Function to scale the assets to a suitable size for the map
def scale_bubble_size(assets):
    return assets / 50000

# Add CircleMarkers with tooltips to the map
for index, row in latest_quarterly_financials.iterrows():
    coordinates = row['zip_coordinates']
    assets_mill = row['assets_mill']
    bank_name = row['name']
    tooltip_text = f"Bank: {bank_name}<br>Coordinates: {coordinates}<br>Assets (millions): {assets_mill}"
    folium.CircleMarker(
        location=coordinates,
        radius=scale_bubble_size(assets_mill),
        color='blue',
        fill=True,
        fill_color='blue',
        fill_opacity=0.5,
        tooltip=tooltip_text,
    ).add_to(map)

# For rendering on Quarto
map.save('heatmap.html')
IFrame(src='heatmap.html', width=700, height=600)

# For rendering in Jupyter Notebook
display(map)
Make this Notebook Trusted to load map: File -> Trust Notebook